In computing, the term minimalism refers to the application of minimalist philosophies and principles in the design and use of hardware and software. Minimalism, in this sense, means designing systems that use the least hardware and software resources possible.
You could compare this with the functioning of the human brain, which exhibits intelligence using the least hardware (sensory organs) and least software (minimal inputs and minimal processing). The human brain demonstrates minimalism in order to rapidly store and synthesize information; which we recognize as rapid thinking and quick reactions.
Our storage and retrieval mechanisms, supported by seemingly automatic computational techniques allowing us to reason and derive answers (often very rapidly), also implies that the brain’s output (intelligence) is derived with a high degree of energy optimization. A straightforward form of organic learning involves the process of mimicking, which can itself be reduced to sequencing and relationships. Forms of training can be reduced to a linear process that replicates action with collected data parameters. If the human brain had to rely on traditional machine-learning techniques to extract patterns for every learning exercise, its energy would drain and lead to malfunction.
However, as human data touch-points are limited, we recognize that the data entities are limited and the entire knowledge structure is created using these attributes. The human brain structures knowledge based on data collected through touch-points (eyes, ears, nose, tongue, and skin) by building relationships and applying weights for basic computations.
The brain employs the most optimized data architecture design with zero redundancy, which enables it to build myriad complex structures using minimum attributes. A hypothetical hierarchical relationship structure may be built within the brain in order to learn and process intelligence in real-time.
Based on the theorem explained above, artificial Intelligence can, therefore, be a direct replica of natural intelligence. The data model that goes into building an intelligent machine is crucial in the utmost for rendering instant learning and response selections. Using sensory data (collected from sensors), it will be vital to lay out a structure for incoming data in order to form patterns that can be matched, given weights, and synthesized.
Using the timestamp of each data record, relationships are built between data based on the hierarchical design, allowing the machine to extract patterns on the fly. These patterns are converted into strings during comparison in the process flow. Using the strength of the node and its cumulative weight, the machine may prioritize which response to select. The diagram below illustrates how a pattern may become highlighted (based on the focus rule) and how it can detect possible associations that help the machine predict the possible outcomes of a repetitive path. The depth of repetition is used to achieve a confirmation state once it attains a certain threshold.
The simplicity demonstrated in selecting an outcome involves simply following the relationship trail. This method (Sequential Covering Rule Building) may assist in arriving at decisions instantaneously without having to parse through redundant computational steps.
To achieve such rapid processing, the data model for AI is required to be centered on the object node, which acts as the pivot between macro-clusters (frame, objects) and micro-clusters (shape, depth, color, etc). This entire data relationship of an object is available as a string. These strings are used to match with the incoming dataset, and the differences and similarities are used for auto-classification and auto-labeling.
The correct data relationship defines the truth behind accurate intelligence. If the holistic relationship of a data entity is not computed to the fullest, there is every possibility that the robot/machine may end up on an erroneous route, which we can see occurring even among naturally intelligent models.
Data relationships backed by the right weights are the two important aspects of accuracy toward the deduction of correct responses. Without this, we could see artificially intelligent machines failing in their learning methods, and resulting in non-intelligence.